ArticleIEEE transactions on medical imaging2019
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Contributions of Artificial Intelligence Reported in Obstetrics and Gynecology Journals: Systematic Review.Journal of medical Internet research · 2022Pooled it
- AI-Driven Three-Dimensional Reconstruction of Fetal Cardiovascular Pathways from Ultrasound Sweep Videos for Congenital Heart Disease Screening.Bioengineering (Basel, Switzerland) · 2026Article
- Review
- Artificial Intelligence-Based Automated Assessment of the Four-Chamber View in Fetal Cardiac Ultrasound Videos.Bioengineering (Basel, Switzerland) · 2026Article
- Three-Dimensional Visualization and Detection of the Pulmonary Venous-Left Atrium Connection Using Artificial Intelligence in Fetal Cardiac Ultrasound Screening.Bioengineering (Basel, Switzerland) · 2026Article
- Identifying visible tissue in intraoperative ultrasound: a method and application.International journal of computer assisted radiology and surgery · 2025Article
- Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology.Journal of clinical medicine · 2023Review
- Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.IEEE transactions on medical imaging · 2022Article
- Weakly- and Semisupervised Probabilistic Segmentation and Quantification of Reverberation Artifacts.BME frontiers · 2022Article
- Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging.Quantitative imaging in medicine and surgery · 2022Article
- Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts.International journal of computer assisted radiology and surgery · 2021Article
- Review
- Towards Standardized Acquisition with a Dual-probe Ultrasound Robot for Fetal Imaging.IEEE robotics and automation letters · 2021Article
- Mutual Information-Based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging.IEEE transactions on medical imaging · 2021Article
- Bone shadow segmentation from ultrasound data for orthopedic surgery using GAN.International journal of computer assisted radiology and surgery · 2020Article
- Deep Learning for Cardiac Image Segmentation: A Review.Frontiers in cardiovascular medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Detecting acoustic shadows in ultrasound images is important in many clinical and engineering applications. Real-time feedback of acoustic shadows can guide sonographers to a standardized diagnostic viewing plane with minimal artifacts and can provide additional information for other automatic image analysis algorithms. However, automatically detecting shadow regions using learning-based algorithms is challenging because pixel-wise ground truth annotation of acoustic shadows is subjective and time consuming. In this paper, we propose a weakly supervised method for automatic confidence estimation of acoustic shadow regions. Our method is able to generate a dense shadow-focused confidence map. In our method, a shadow-seg module is built to learn general shadow features for shadow segmentation, based on global image-level annotations as well as a small number of coarse pixel-wise shadow annotations. A transfer function is introduced to extend the obtained binary shadow segmentation to a reference confidence map. In addition, a confidence estimation network is proposed to learn the mapping between input images and the reference confidence maps. This network is able to predict shadow confidence maps directly from input images during inference. We use evaluation metrics such as DICE, inter-class correlation, and so on, to verify the effectiveness of our method. Our method is more consistent than human annotation and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions. Furthermore, we demonstrate the applicability of our method by integrating shadow confidence maps into tasks such as ultrasound image classification, multi-view image fusion, and automated biometric measurements.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.